Automatic urban scene object recognition refers to the process of segmentation and classifying of objects of interest in an image into predefined semantic labels, such as “building”, “tree” or “road”. This typically involves a fixed number of object categories, each of which requires a training model for classifying image segments. While many techniques for two-dimensional (2D) object recognition have been proposed, the accuracy of these systems is to some extent unsatisfactory, because 2D image cues are sensitive to varying imaging conditions such as lighting, shadow etc.
Three-dimensional (3D) object recognition systems using laser scanning, such as Light Detection And Ranging (LiDAR), provide an output of 3D point clouds. 3D point clouds can be used for a number of applications, such as rendering appealing visual effect based on the physical properties of 3D structures and cleaning of raw input 3D point clouds e.g. by removing moving objects (car, bike, person).
However, identifying and recognizing objects despite appearance variation (change in e.g. texture, color or illumination) has turned out to be a surprisingly difficult task for computer vision systems. In the field of the 3D sensing technologies (such as LiDAR), a further challenge in organizing and managing the data is provided due to a huge amount of 3D point cloud data together with the limitations of computer hardware.
A further problem relates to the computational burden of 3D modelling methods based on the laser scanned image data. Typically the 3D point cloud is registered with image data, automatic plane detection is used for surface modelling and texture mapping is done using image data. There is a need for efficiency improvements in this process.